Update README.md

README.md

@@ -14,7 +14,7 @@ For more details, including benchmark evaluation, hardware requirements, and inf
 refer to our [Github](https://github.com/Y-Research-SBU/CSRv2).
 
 ## Sentence Transformer Usage
-You can evaluate this model loaded by Sentence Transformers with the following code snippet (take SciDocsRR as one example):
+You can evaluate this model loaded by Sentence Transformers with the following code snippet (take ArXivHierarchicalClusteringS2S as one example):
 ```python
 import mteb
 from sentence_transformers import SparseEncoder
@@ -23,14 +23,14 @@ model = SparseEncoder(
     trust_remote_code=True
 )
 model.prompts = {
-    "SciDocsRR": "Instruct: Given a title of a scientific paper, retrieve the titles of other relevant papers\n Query:"  
+    "ArXivHierarchicalClusteringS2S": "Instruct: Identify the main and secondary category of Arxiv papers based on the titles\n Query:"  
 }
-task = mteb.get_tasks(tasks=["SciDocsRR"])
+task = mteb.get_tasks(tasks=["ArXivHierarchicalClusteringS2S"])
 evaluation = mteb.MTEB(tasks=task)
 evaluation.run(
     model,
     eval_splits=["test"],
-    output_folder="./results/SciDocsRR",
+    output_folder="./results/ArXivHierarchicalClusteringS2S",
     show_progress_bar=True
     encode_kwargs={"convert_to_sparse_tensor": False, "batch_size": 8}
 )  # MTEB don't support sparse tensors yet, so we need to convert to dense tensors